Identifying and differentiating bacteria based on their emitted volatile organic compounds (VOCs) opens vast opportunities for rapid diagnostics. Secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS) is an ideal technique for VOC-biomarker discovery because of its speed, sensitivity towards polar molecules and compound characterization possibilities. Here, an in vitro SESI-HRMS workflow to find biomarkers for cystic fibrosis (CF)-related pathogens P. aeruginosa, S. pneumoniae, S. aureus, H. influenzae, E. coli and S. maltophilia is described. From 180 headspace samples, the six pathogens are distinguishable in the first three principal components and predictive analysis with a support vector machine algorithm using leave-one-out cross-validation exhibited perfect accuracy scores for the differentiation between the groups. Additionally, 94 distinctive features were found by recursive feature elimination and further characterized by SESI-MS/MS, which yielded 33 putatively identified biomarkers. In conclusion, the six pathogens can be distinguished in vitro based on their VOC profiles as well as the herein reported putative biomarkers. In the future, these putative biomarkers might be helpful for pathogen detection in vivo based on breath samples from patients with CF.

Environmental volatile organic compounds (VOCs) from the ambient air potentially influence on-line breath analysis measurements by secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS). The aim of this study was to investigate how inhaling through a VOC filter affects the detected breath profiles and whether it is feasible to integrate such filters into routine measurements. A total of 24 adult participants performed paired breath analysis measurements with and without the use of an activated carbon filter for inspiration. Concordance correlation coefficients (CCCs) and the Bland–Altman analysis were used to assess the agreement between the two methods. Additionally, the effect on a selection of known metabolites and contaminants was analyzed. Out of all the detected features, 78.3% showed at least a moderate agreement before and after filter usage (CCC > 0.9). The decrease in agreement of the remaining m/z features was mostly associated with reduced signal intensities after filter usage. Although a moderate-to-substantial concordance was found for almost 80% of the m/z features, the filter still had an effect by decreasing signal intensities, not only for contaminants, but also for some of the studied metabolites. Operationally, the use of the filter complicated and slowed down the conductance of measurements, limiting its applicability in clinical studies.

The early detection of inflammation and infection is important to prevent irreversible lung damage in cystic fibrosis. Novel and non-invasive monitoring tools would be of high benefit for the quality of life of patients. Our group previously detected over 100 exhaled mass-to-charge (m/z) features, using on-line secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS), which distinguish children with cystic fibrosis from healthy controls. The aim of this study was to annotate as many m/z features as possible with putative chemical structures. Compound identification was performed by applying a rigorous workflow, which included the analysis of on-line MS² spectra and a literature comparison. A total of 49 discriminatory exhaled compounds were putatively identified. A group of compounds including glycolic acid, glyceric acid and xanthine were elevated in the cystic fibrosis group. A large group of acylcarnitines and aldehydes were found to be decreased in cystic fibrosis. The proposed compound identification workflow was used to identify signatures of volatile organic compounds that discriminate children with cystic fibrosis from healthy controls, which is the first step for future non-invasive and personalized applications.

Introduction: Enhancing paediatric asthma diagnosis is crucial. Molecular analysis of exhaled breath is a rapidly evolving field aimed at harnessing established and innovative technologies for clinical applications. This study evaluates the feasibility of using online proton-transfer-reaction mass spectrometry (PTR-MS) to identify distinctive breath signatures in children with allergic asthma. Methods: Exhaled breath samples of 81 children (41 with allergic asthma and 40 healthy controls) were analysed using the Vocus CI-TOF mass spectrometer (Tofwerk AG, Switzerland), with mass spectra acquired in H ₃ O ⁺ and NH ₄ ⁺ ionisation modes. Significant mass-to-charge (m/z) features were extracted using the Wilcoxon rank-sum test. Molecular identification was conducted using two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC-Q-TOF). Results: Statistical analysis revealed 89 significant m/z features associated with paediatric allergic asthma, 66 in H ₃ O ⁺ mode and 23 in NH ₄ ⁺ mode. Supervised machine learning achieved an average accuracy of 74.7% in distinguishing between the groups. GCxGC-QTOF analysis identified a subset of significant features, including four previously reported asthma predictors in breath analysis studies. 16 novel asthma predictor candidates were additionally detected, including 7 likely endogenous, 4 unknowns and 3 exogenous. The main group of breath metabolites was structurally related fatty acids, methyl esters and aldehydes, including four known biomarkers of lipid peroxidation. Conclusion: Our findings demonstrate the suitability of PTR-MS for real-time breath analysis in paediatric populations. Moreover, the identification of distinct breath signatures exclusive to allergic asthma in children suggests the potential of leveraging such technology for non-invasive diagnostic applications.

Breath profile analysis of volatile organic compounds (VOCs) by electronic nose (eNose) technology can distinguish between healthy individuals and people with cystic fibrosis (pwCF). It remains, however, unclear whether CFTR modulator therapy, that corrects the underlying defect in treated pwCF, alters the exhaled breath profiles. In this prospective longitudinal study, eNose-derived exhaled breath profiles were obtained using the SpiroNose® (Breathomix, Leiden, The Netherlands) from 61 CF children before starting therapy with elexacaftor/tezacaftor/ivacaftor (ETI), and at follow-up visits less than 3 and more than 6 months after ETI initiation. Routine clinical outcomes and airway microbiology were assessed at all visits. Twenty-six healthy controls provided single measurements. Longitudinal SpiroNose sensor data were analyzed using linear mixed-effects models and permutational multivariate analysis of variance (PERMANOVA), and exploratory mixed-effects models to test for associations with Staphylococcus aureus (SA) airway infection and clearance. ETI led to improved sweat chloride concentrations, pulmonary function (ppFEV₁) and BMI, but no significant changes in SpiroNose sensor signals were observed during follow-up. SpiroNose sensor signals reliably discriminated healthy controls from CF children at baseline and at follow-up on ETI, with consistent differences in multiple SpiroNose sensors. SA airway infection was captured (sensor S7), whereas SA clearance did not result in measurable changes in SpiroNose signals. CFTR modulator therapy with ETI does not significantly alter eNose-derived VOC breath profiles in children with CF. The ability of the eNose to distinguish CF from healthy controls remains unchanged after ETI initiation.

Diffusing capacity for carbon monoxide (DLco) testing is widely used in adult respiratory medicine, but its role in pediatric care remains unclear. This st